Dynamic behavior analysis and ensemble learning for credit card attrition prediction

Autor:	Болин Чен
Jazyk:	English<br />Russian
Rok vydání:	2023
Předmět:	customer attrition churn prediction credit card customer retention transaction sequence modeling temporal dynamics modeling Technology (General) T1-995
Zdroj:	Современные инновации, системы и технологии, Vol 3, Iss 4 (2023)
Druh dokumentu:	article
ISSN:	2782-2818 2782-2826
DOI:	10.47813/2782-2818-2023-3-4-0109-0118
Popis:	Credit card attrition imposes a substantial business cost for financial institutions. Early and accurate prediction of customer churn allows banks to take proactive retention measures. However, modeling credit card attrition presents complex challenges given evolutionary customer spending behaviors. This paper puts forth a robust methodology harnessing dynamic behavior analysis along with ensemble learning to capture non-static patterns in transaction data. Explainability techniques further enable interpretation of attrition likelihood on an individual customer basis. Rigorous experiments demonstrate significant predictive performance improvements attained using the proposed approach.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/8b4ca386f9124c4487385bfbd0d4e053 Zobrazit plný text záznamu View record in DOAJ